Predicting Commitment and Self-Regulation Based on Identity Processing Styles

The purpose of this study was to predict commitment and self-regulation based on identity processing styles among students of Hormozgan University. For this purpose, a sample of 422 people (in terms of gender ratio, educational groups, educational levels, and different academic years) were selected by stratified random sampling method from Hormozgan University students. The following questionnaires were used to collect data: Identity Processing Styles Questionnaire, Revised Commitment Scale, and a shortened form of a self-regulatory questionnaire. The data obtained from this study were analyzed using SPSS 22 and AMOS 24 software, and MANOVA, and multiple regression statistical methods. The results of multiple regression analysis showed that the prediction of commitment and self-regulation through identity processing styles was statistically significant at the level of 0.001. There was also a relationship between commitment and self-regulatory variables with identity processing styles, and the obtained coefficients showed that an equation could be presented based on identity processing styles to predict commitment and self-regulation.